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Metal distribution, bioavailability and isotope variations ...
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ORIGINAL PAPER
Metal distribution, bioavailability and isotope variationsin polluted soils from Lower Swansea Valley, UK
Kathrin Schilling . Anirban Basu . Alicia Kaplan . William T. Perkins
Received: 16 July 2020 / Accepted: 8 December 2020
� The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021
Abstract Soils in the Lower Swansea Valley,
(United Kingdom) contain elevated level of metals,
enough to cause direct or indirect effects on human
health. This study assesses the severity of soil pollution
and bioavailability of Cu and other metals (Ni, Zn, Co,
Pb and Cr) in soils with various distances from a Ni
refinery. We compare Cu concentrations in opera-
tionally defined soil fractions (bioavailable, bound to
Fe/Mn oxide and incorporated in organic matter) with
other metals (Ni, Zn, Pb, Co, Cr) usually occurring in
ores used in metallurgic processes and report their
pollution and geoaccumulation indices (PI and Igeo).
Further, we useCu stable isotope ratios (d65Cu) to tracethe fate and mobility of Cu in soils. Our data suggest a
point source of contamination for some of the heavy
metals including Ni (Igeo = 1.9), Zn (Igeo = 0.28) and
Cu (Igeo = 3.6) near the Ni refinery. However, Co
(Igeo = 0.15) and Pb (Igeo = 3.3) contaminations are
likely to be linked to different sources. No elevated Cr
levels (Igeo= -0.07) occur in any of the studied soils. All
soil metals are predominantly associated with organic
matter ([50%) which reduces their bioavailibility and
thus their risk for ecological and human health. TheCu
isotope data show that Cu in soil organic matter is
enriched in 65Cu, while the lighter isotopes (63Cu)
remain in the dissolved bioavailable Cu fraction
(D65Cuorganic-bioavailable is ?0.12 ± 0.13%). This sug-
gests the preferential complexation of 65Cu with soil
organic matter after dissolution of Cu deposited to the
soil. Thus, Cu isotope data can effectively indicate
pathways of metal migration in polluted soils.
Keywords Heavy metals � Soil � Spatialdistribution � Bioavailability � Copper isotopes �Pollution
Introduction
In the EU, about 45% of the soils are estimated to be
contaminated (van Liedekerke et al. 2014). Heavy
Supplementary Information The online version of thisarticle (https://doi.org/10.1007/s10653-020-00794-x) containssupplementary material, which is available to authorized users.
K. Schilling (&)
Lamont-Doherty Earth Observatory, Columbia
University, Palisades 10964, NY, USA
e-mail: [email protected]
A. Basu
Department of Earth Sciences, Royal Holloway,
University of London, Egham TW20 0EX, United
Kingdom
A. Kaplan
Department of Earth Sciences, University of Oxford,
South Parks Road, Oxford OX1 3AN, United Kingdom
W. T. Perkins
Department of Geography and Earth Sciences,
Aberystwyth University, Aberystwyth,
Ceredigion SY23 3DB, United Kingdom
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https://doi.org/10.1007/s10653-020-00794-x(0123456789().,-volV)( 0123456789().,-volV)
metals are with 35% the major contaminants in soils.
Anthropogenic activity including mining, smelting
and refining significantly redistributes heavy metals in
the environment. Despite the biological role of some
heavy metals, most are toxic at high concentrations
(Hodson and Donner 2013). This means if heavy
metals are bioavailable, they cause large ecological
damage (Hutchinson and Whitby 1974; Kabata-Pen-
dias 2010; Kim et al. 2015) and bear a significant
health risk for humans (Hodson and Donner 2013;
Entwistle et al. 2019).
Human health risk from heavy metals in soils might
even increase due to a changing climate. Changing
climate can alter the physicochemical soil properties
and soil processes resulting in an increase of dissolved
and bioavailable heavy metals (Rieuwerts et al. 1998;
Gonzalez-Alcaraz and van Gestel 2015; Grobelak and
Kowalska 2020; Jarsjo et al. 2020). In particular, soil
metals from anthropogenic sources are more bioavail-
able than those of pedogenic origin (Kaasalainen and
Yli-Halla 2003; Kabata-Pendias 2010). To predict
potential changes in the bioavailability of highly toxic
metals in a changing climate, it is crucial to assess the
current metal status in soils including the spatial metal
distribution, speciation and phase association, and
mechanisms controlling the spatial metal distribution.
As threshold values of metal contaminants for most
national and international guidelines [e.g., Soil Guide-
line Values (SGVs)] are based on total soil metal
content, these guidelines provide only limited infor-
mation about the metal bioavailability. Additionally,
soils are compositionally heterogeneous, and thus
changes in metal bioavailability largely depend on the
metal speciation and phase association.
Specifically, soils in regions with long history of
metallurgical industry are often contaminated with
heavy metals. The Lower Swansea Valley has been
affected by metal smelting and refining since the
eighteenth century up to the present day (Perkins 2011).
During the eighteenth century, two-thirds of copper in
the UK was processed in smelters at this region
(Marchant et al. 2011; Perkins 2011). Nickel refining
began in the early twentieth century. Despite a decline
of the metal industry in the Lower Swansea Valley
during the 1920s and 1930s,with the closing ofmany of
the metalworks (Alban 1984), the nickel refinery for
instance in the village Clydach is still operational.
From all metals, specifically Cu tends to accumu-
late in topsoils in areas with intensive mining and/or
metallurgic activity (Hutchinson and Whitby 1974,
Temple and Bisessar 1981, Ettler et al. 2011). Copper
is normally found in unpolluted soils at concentrations
below 30 lg g-1 (Artiola 2005) but becomes detri-
mental to wildlife above 60 lg Cu g-1 (Kabata-
Pendias et al. 1981, Alloway 2012, Kabata-Pendias
2010). With an estimated soil residence time between
1000 and 3000 years for temperate climates (Bowen
1979), Cu soil pollution becomes a long-term ecolog-
ical issue.
Previous studies have demonstrated that Cu isotope
signature is an excellent environmental tracer to
identify Cu sources and understand the Cu cycling in
human-impacted areas (Mathur et al. 2009; Bigalke
et al. 2010a; Thapalia et al. 2010; Fekiacova et al.
2015; Kribek et al. 2018; Mihaljevic et al. 2018).
Fractionation of Cu isotopes can change the isotopic
signature through a variety of low-temperature pro-
cesses both biological and abiotic. Abiotic isotope
fractionation is caused by adsorption onto mineral
surfaces (Balistrieri et al. 2008; Pokrovsky et al. 2008)
or incorporation of Cu into organic matter (Bigalke
et al. 2010b; Ryan et al. 2014). Biological processes
include the isotopic fractionation of Cu by plants,
which have a systematic preference of taking up the
light isotope (63Cu), leaving a soil enriched in 65Cu
(Weinstein et al. 2011). Furthermore, sequential
extraction combined with Cu stable isotope analysis
has been successfully applied to determine Cu release
in a soil flooding event (Kusonwiriyawong et al. 2016)
and to monitor the mobilization of Cu from mine
tailing sediments (Roebbert et al. 2018). Thus, Cu
isotope analysis can provide a better understanding of
the biogeochemical soil cycling of Cu (Bigalke et al.
2010a, Babcsanyi et al. 2016; Vance et al. 2008).
The objectives of this study are (1) to characterize
the spatial distribution of metals in soils in the vicinity
of a refinery (2) to assess the environmental risk
factors by quantifying metal fractions associated with
different soil constituents and (3) to unravel the fate of
soil Cu using Cu isotope composition (d65Cu) of
operationally defined Cu soil pools. The results of this
study help identify potential sources of heavy metals
as by-products of the metallurgic processes in the
Lower Swansea Valley relative to the natural back-
ground signal, and to trace pathways of metal migra-
tion. Additionally, our results provide a way to
evaluate the environmental quality and environmental
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health of the Lower Swansea Valley, an area with a
long history of metal refining.
Materials and methods
Study site and soil sampling
The study site is located in South Wales in the Lower
Swansea Valley, approximately 10 km northeast of
Swansea city (Fig. 1). Soil samples were collected
from various locations within the vicinity of the Ni
refinery in the village Clydach (Fig. 1, Table S1). Soils
in the Swansea Valley are primarily formed from the
product of glacial erosion of Carboniferous sedimen-
tary rocks generating sandy clay loam soils. A control
sample was selected from an uncontaminated location
about 30 km ‘downwind’ of the Ni refinery, an area of
Devonian rocks (Old Red Sandstone). For sample
collection, vegetation was removed from the soil
surface and soil was collected from the upper 5 cm.
We assume that topsoils are mainly impacted by met-
als originated from long-term metallurgical industry
and are derived to a smaller extent from the bedrock.
The collected soils were dried for 48 h at 40 �C.Mortar and pestle were used to disaggregate the
soil and create a homogeneous texture. The dried soils
were stored in clear plastic bags prior to further
analysis.
Sequential extraction procedure (SEP)
To examine the heavy metal speciation and phase
association, we used the well-established BCR extrac-
tion technique (Tessier 1979; Davidson et al. 1998;
Rauret et al. 1999). One gram of air-dried soil was
weighed into a 50-ml metal-free centrifuge tube
(VWR). Then, for the first extraction step (= ex-
changeable and bioavailable fraction), 30 ml of 0.1 M
acetic acid was added and shaken on a rotary shaker at
room temperature overnight. Afterward, the sample
was centrifuged at 3000 rpm (Eppendorf Centrifuge
5810R) for 20 min, the supernatant was decanted and
the soil residual was washed with 20 ml of double-
deionized water. Subsequently, 30 ml of 0.5 M
hydroxylamine hydroxide was added to the soil
residual to extract the reducible fraction, namely
metals associated with Fe/Mn (hydr)oxides. Similar
to the first extraction step, the samples were shaken
overnight, centrifuged, the supernatant was decanted
and the soil residual was washed with double-deion-
ized water. For the third step, 5 ml of 8.8 M hydrogen
peroxide (Romil Ltd, Cambridge, UK) was added to
the soil and left at room temperature for 1 h. The
samples were then placed on a hot plate at 85 �C to
reduce the sample volume to less than 3 ml. The
hydrogen peroxide step was repeated twice before
adding 20 ml of 1 M ammonium acetate (adjusted to
pH 4) and shaken overnight at 40 rpm. Afterward, the
sample was centrifuged at 3000 rpm (Eppendorf
Fig. 1 Sampling location
map adapted from Perkins
(2011)
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Centrifuge 5810R) for 20 min, the supernatant was
decanted and the soil residual was washed with 20 ml
of double-deionized water. The supernatants from all
extraction steps were filtered through 0.45 lmsyringe membrane filters. Procedural blanks were
prepared to estimate the metal content in the reagents
used for each SEP step.
For reproducibility check and validation of the
BCR method, triplicate extractions (n = 5) were
performed using the reference material NIST 2711a,
a moderately contaminated Montana soil. We selected
the certified reference soil NIST 2711a because the
BCR extraction method has been performed and
assessed previously for several metals (Sutherland
and Tack 2002; Kubova et al. 2004; Larner et al.
2006). The triplicate extractions of the three SEP steps
show a good reproducibility (Table S2) with relative
standard deviation (RSD) for the three fractions
(bioavailable and exchangeable, Fe/Mn oxide-bound
and organically bound metals) of less than\ 10%.
Further, the metal concentrations for the three indi-
vidual fractions are in good agreement with previously
reported data for NIST 2711a (Sutherland and Tack
2002; Kubova et al. 2004; Larner et al. 2006).
Concentration analysis
The concentrations of Cu, Ni, Zn, Co, Pb and Cr in
the sequentially extracted soil fractions were mea-
sured using a PerkinElmer NexION 350D inductively
coupled plasma-mass spectrometer (ICP-MS)
equipped with an Elemental Scientific (Omaha,
USA) prepFASTM5 autosampler and autodiluter. A
calibration curve was calculated using premade cali-
bration solutions with varying concentrations of each
metal. After every 11 samples, standard quality
control and calibration blanks were analyzed to
evaluate potential memory effects and cross contam-
ination. Procedural blanks are very low or below
detection levels of the analyzed metals (\ 6% relative
to control sample with lowest metal concentra-
tions), and confirm that the reagents and extraction
procedure do not contribute significantly to the final
metal concentration in the sample. Uncertainties
are * 2% (1SD) for the majority of measurements.
Cu isotope analysis
Subsamples of the sequentially extracted soil frac-
tions (exchangeable/bioavailable, bound to Fe/Mn
oxide and incorporated in organic matter) were
analyzed for Cu isotopes. The samples were purified
using ion-exchange chromatography to separate Cu
from the sample matrix prior isotope analysis. Briefly,
Cu separation was performed with AG MP-1 resin
(100–200 mesh, BioRad) using 250–300 lL resin
volume. The columns were first cleaned with 10 ml of
0.1 M HNO3 and 10 ml of double-deionized water,
conditioned with 8 M HCl ? 0.001% H2O2 and
equilibrated with 4 9 2 ml 8 M HCl ? 0.001%
H2O2. The sample re-dissolved in 1 ml 8 M HCl ?
0.001% H2O2 was loaded on the column and subse-
quently rinsed with 3 ml of 8 MHCl ? 0.001%H2O2.
In the last step, 9 ml of 8 M HCl ? 0.001% H2O2 was
added to elute Cu from the resin. Each sample was put
through the column twice in order to produce an eluate
Cu fraction that is pure enough to yield accurate
Cu isotope measurements. The procedural blank for
Cu was B 1.9% of total Cu. The Cu isotope ratio
(65Cu/63Cu) was measured with a Nu Plasma HRMC-
ICP-MS (Nu Instruments, Wrexham, UK) and a
DSN desolvating sample introduction system. Copper
isotope measurement of each sample was performed
at low mass resolution and normalized using sample-
standard bracketing. The Cu isotope values were
expressed in the delta notation as d65Cu (%) relative
to the NIST 976:
d65Cu ¼65Cu=63Cu� �
sample
65Cu=63Cu� �
NIST976
!
� 1000 ð1Þ
The precision of d65Cu measurement
is ± 0.12%, estimated using twice the root-mean-
square for samples prepared and analyzed in repli-
cates (n = 6). The reproducibility of d65Cu for
replicate SEP of NIST 2711a is ± 0.08% for Fe/Mn
oxide-bound Cu (n = 2) and ± 0.08% for organically
bound Cu fraction (n = 4).
Spatial distribution and health risk assessment
of metals
The total metal soil content (sum of all sequentially
extracted soil fractions) and the bioavailable soil metal
fraction were used as input data to produce distribution
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maps using QGIS version 3.11. Further, we deter-
mined the pollution index (PI) and the geoaccumula-
tion index (Igeo) for the total soil metal content.
The pollution index (PI) was defined as:
PI ¼ Soilmetal=Cmetal ð2Þ
where Soilmetal is the concentration of each metal in
the topsoil collected around the refinery and Cmetal is
the metal concentration in the control soil (= back-
ground value of each metal), which is located * 30
km ‘downwind’ from the refinery. The level of
pollution is classified on a scale from 1 to 5 and
specified in Table S3.
The geoaccumulation index Igeo was determined as
described by Muller (1969):
Igeo ¼ log2Soilmetal
1:5� Cmetal
ð3Þ
where Soilmetal is the concentration of each metal in
the topsoil and Cmetal is the metal concentration in the
control soil (= background value).
Results
Soil metal concentrations and distribution
The spatial distribution for the total soil concentration
of each metal and their bioavailable fraction are shown
in Fig. 2 and Fig. 3. The median total soil concentra-
tion of metals follows the pattern of Cu[ Pb[Zn[Ni[Cr[Co. The bioavailable soil fraction shows
that the concentration decreases in the order of
Zn[Ni[Cu[Co[ Pb[Cr. A detailed descrip-
tion of concentration, distribution, and bioavailability
for each metal is given in the following section.
Copper
The spatial distribution map shows the highest soil Cu
concentrations closest to the refinery and in the village
of Clydach (Fig. 2). The total Cu soil concentrations
range between 64.5 and 365.8 lg g-1 (median
Cu = 202 lg g-1) and decreases systematically with
distance from the refinery (Fig. 4a). The background
Cu content measured in soils from the control site is
11.6 lg g-1 which is almost 20 times lower than the
median Cu soil content observed at the study site.
Bioavailable Cu and Cu bound on Fe/Mn (hydr)oxides
account for 4.6% and 0.5% of total Cu in the topsoils.
Bioavailable Cu has with 33.7 lg Cu g-1 soil the
highest concentration northeast of the refinery
(Fig. 3). The bioavailable (exchangeable) Cu shows
a strong negative correlation with pH (Fig. 5a). The
largest Cu soil fraction is associated with organic
matter ([ 95%, Table S2) with a strong positive
correlation with soil Corg (r2 = 0.80) (Fig. 5b). The
mean PI and Igeo indicate extreme soil contamination
with Cu (Fig. 5). The Cu concentrations correlate with
Ni (r2 = 0.70) soil concentrations and show similar
spatial distribution pattern.
Nickel
The highest Ni soil concentration of 230.7 lg Ni g-1
occurs northeast of the refinery (Fig. 2). Total Ni soil
concentrations range between 12.6 and 230.7 lg g-1
(median Ni = 70 lg g-1) and decrease with increas-
ing distance to the refinery (Fig. 4b). The median Ni
soil concentration around the refinery is nearly four
times higher than the average Ni value of 20 lg g-1
reported for English and Welsh soils (McGrath and
Loveland 1992; UK Soil and Herbage Pollutant
Survey (UKSHS) 2007). Nickel in the topsoils around
the refinery is up to 20 times higher compared to the
control soil. The median Ni soil concentration is about
three times lower than the median Cu soil concentra-
tion. The largest Ni soil fraction is associated with
organic matter ([ 72%; Figure S1). Bioavailable Ni
accounts on average for 23% of the total Ni soil
content and reaches the highest concentration of 42.7
lg Ni g-1 northeast of the refinery (Fig. 3). Nickel
bound on Fe/Mn (hydr)oxides accounts for 5.3% of
the total Ni soil content. Median Igeo and PI indices for
total Ni soil content describe moderate to very strong
Ni contamination (Fig. 6).
Zinc
The highest Zn concentration in the topsoils occurs
northeast of the refinery (2457 lg g-1) (Fig. 2). Zinc
soil concentrations range between 11.6 and
2457 lg g-1 (median Zn = 115 lg g-1). The median
soil content of Zn is approximately 1.5 times higher
than that at the control site (79 lg g-1) and the
average value of 88 lg g-1 for Welsh soils (UKSHS
Survey 2007). On average, 37% of total soil Zn is
present as bioavailable Zn with the highest
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concentration of 812 lg Zn g-1 northeast of the
refinery (Fig. 3). Organically bound Zn fraction
accounts for about 52% of the total Zn, while Zn
associated with Fe/Mn (hydr)oxides is about 10%
(Figure S1). Mean PI and Igeo show that the soils
around the refinery are not contaminated with Zn
(Fig. 6). Only the sampling location northeast of the
refinery can be classified as extremely Zn
contaminated.
Cobalt
The spatial distribution map shows the highest soil Co
concentrations southwest of the refinery (37.4 lg g-1;
Fig. 2). Total soil Co ranges between 3.9 and
37.4 lg g-1 (median Zn = 10 lg g-1). The median
Co soil concentration is about 1.5 times higher than in
the topsoil at the control site (6.6 lg g-1). Cobalt
at all sampling locations is above the global Co soil
concentration of 7.9 lg g-1 (Kabata-Pendias 2010).
The largest Co soil fraction is associated with organic
matter (50%; Figure S1). Bioavailable Co and Co
bound on Fe/Mn (hydr)oxides account in average for
35% and 16% of the total Co soil content. The spatial
distribution reveals the highest bioavailable Co soil
concentration of 12.3 lg Co g-1 in the village Clydach
(Fig. 3). The average PI and Igeo for the soils in vicinity
of the refinery belong to uncontaminated to moder-
ately contaminated category (Fig. 6). Despite a strong
correlation between Co and total Ni (r2 = 0.73) and a
moderate correlation between Co and total Cu
(r2 = 0.65), the spatial distribution pattern of Co
concentrations does not match that of Ni or Cu.
Similar to Ni, organically bound Co correlates
strongly with the soil Corg (r2 = 0.75).
Lead
The highest Pb soil concentration (595 lg g-1) occurs
southwest of the refinery at the Clydach village
Fig. 2 Spatial distribution maps for total copper, nickel, zinc,
cobalt, lead and chromium in topsoils. Dark color indicates
highest soil metal content and bright color indicates the lowest
soil metal content. Yellow star represents the location of the
smelter; red dots show the individual sampling locations
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(Fig. 2). Total Pb soil concentrations range between
42.9 and 595 lg g-1 (median Pb = 118 lg g-). The
median Pb soil concentration is more than an order of
magnitude greater than for the soil at the control site
(8.3 lg g-1) and exceeds the average value of Welsh
soils of 59.2 lg g-1 (UKSHS Survey 2007). On
average, 95% of Pb is associated with soil organic
matter. Bioavailable Pb and Pb bound on Fe/Mn
(hydr)oxides are with 1.6% and 4.3% very low
(Figure S1). The spatial distrubtion shows the highest
bioavailable Pb soil concentration of 9.3 lg Pb g-1 in
the village Clydach. The PI and Igeo indices for Pb
classify the soils as heavily to extremely contaminated
(Fig. 6). Total Pb soil concentration shows no corre-
lation with Ni, Cu, Cr, Zn and Co.
Chromium
The median Cr concentration in the soil around the
refinery is with 11.6 lg g-1 almost similar to the
total Cr at the control site (9.1 lg g-1). Total Cr in all
soils is below the average value of Welsh soils of
25.5 lg g-1 (UKSHS Survey 2007). The spatial
distribution map of Cr shows the maximum soil Cr
content northeast of the refinery (Fig. 2). Chromium is
with more than 98% predominantly associated with
soil organic matter (Figure S1). The bioavailable and
Fe/Mn (hydr)oxides bound on Cr account for less than
2% of the total soil Cr. The spatial distrubtion map for
the bioavailable Cr fraction shows the highest con-
centration of 0.7 lg Cr g-1 northeast of the refinry
(Fig. 3). The average PI and Igeo for Cr can be
classified as uncontaminated (Fig. 6).
d65Cu for different soil fractions
The Cu isotopic composition of the three sequentially
extracted soil fractions is reported in Table 1. The
d65Cu values for organically bound Cu vary between -0.19 and ?0.10%. The d65Cu values of bioavailable
Cu range between - 0.43 and ?0.22%, and d65Cuvalues of the Fe/Mn (hydr)oxides-bound fraction vary
Fig. 3 Spatial distribution maps for bioavailable fraction of
copper, nickel, zinc, cobalt, lead and chromium in topsoils. Dark
color indicates highest soil metal content and bright color
indicates the lowest soil metal content. Yellow star represents
the location of the smelter; red dots = sampling location
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between - 0.43 and ?0.07%. When pooled together,
d65Cu values for the various soil fractions show a clear
trend (Fig. 7a). In each sample, the bioavailable Cu is
isotopically lighter than the organically bound Cu
(Fig. 7a). Further, there is a strong positive correlation
between d65Cu of bioavailable and organically bound
Cu (r2 = 0.76; Fig. 7b).
Discussion
Assessment of metal pollution in the soils
Most soil metal concentrations (except Cr) reach
potentially harmful levels at multiple sampling loca-
tions. Nickel, Zn and Cu show the highest soil
concentrations in samples northeast of the refinery,
while the highest concentrations of Co and Pb are
found southwest of the refinery. The Igeo values for Ni,
Zn, Cu, Pb and Co indicate soil pollution of different
magnitude. While the median Cu and Pb soil concen-
trations can be classified as heavily contaminated
(Igeo\ 3), Ni shows on average moderate contamina-
tion levels (Igeo = 2.1). Cobalt and Zn reflect rather
disperse patterns of contamination ranging from
uncontaminated to heavily contaminated levels
depending on the sampling location. This shows that
a clear risk assessment of soil contamination levels for
Co and Zn is difficult in the Lower Swansea Valley.
Metal source tracing
The spatial distribution patterns delineate two well-
defined sources (Figs. 2 and 3). The spatial distribution
(A)
(B)
R² = 0.6378
05
101520253035404550
0 1000 2000 3000
Bioa
vaila
ble
Cu
soil
cont
ent (
µg g
-1)
Distance to refinery (m)
R² = 0.3777
05
101520253035404550
0 500 1000 1500 2000 2500
Bio
avai
labl
e N
i fra
ctio
n(µ
g g-
1 )
Distance to refinery (m)
Fig. 4 Concentration of bioavailable soil fraction of A) copper
and B) nickel as a function of distance (in m) to the Ni refinery in
Clydach. Dashed lines are the best fitted curve described as
y = ax-b and the correlation coefficient (R2)
(A)
(B)
R² = 0.7135
0
5
10
15
20
25
30
35
40
4 5 6 7 8
Bioa
vaila
ble
Cu
(µg
g-1 )
Soil pH
R² = 0.7983
0
50
100
150
200
250
300
350
400
0 10 20 30
Org
anic
ally
-bou
nd C
u(µ
g g-
1 )
Soil Corg(%)
Fig. 5 Relationship between a soil pH and bioavailable Cu soil
fractions and b soil organic carbon and organically bound Cu
soil fraction. The dashed lines describe the best fit of a linear
regression and the correlation coefficient (R2)
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pattern of Ni, Cu, Cr and Zn suggest an identical point
source of pollution near the refinery. A previous study
has shown that in particular the elevated soil content of
Cu and Ni is linked to historic activities of smelting
and refining at the Lower Swansea Valley (Davies
1997). The Ni refinery at Clydach is over 100 years in
operation and processes Ni–Cu–sulfides from Sud-
bury, Canada. Sudbury sulfide ores contain large
amount of Ni and Cu with minor constituents of Zn, Cr
and Pb (Hawley 1962; Adamo et al. 2002). Previous
studies have demonstrated elevated levels of metals in
soils associated with the Ni and Cu smelting in
Sudbury, Canada (Hutchinson and Whitby 1974;
Freedman and Hutchinson 1980; Adamo et al. 2002),
and it is plausible that similar ore processing activities
over time have led to elevated soil metal concentra-
tions at our study site.
The decreasing concentrations of Cu, Ni, Cr and Zn
in the soils with increasing distance from the refinery
are consistent with a wind driven mode of transport.
Accordingly, the highest Cu and Ni concentrations in
the soil occur closest to the refinery, with a logarithmic
dependence of soil concentrations with distance to the
point source (Fig. 4). We hypothesize that the spatial
distribution pattern of Ni, Cu, Cr and Zn is caused by
the predominantly SW prevailing wind. It is known
that fine particulates (\ 2.5 lm) originating from
smelting and refining can be transported over long
distances (Entwistle et al. 2019). Our data of the
spatial distribution of these metals in topsoils around
the refinery are in good agreement with the modeled
Fig. 6 a Index of geoaccumulation Igeo and b pollution index
(PI) of the total soil metal content (Cr, Pb, Zn, Cu, Ni and Co)
Table 1 Cu concentrations (lg g-1) and d65Cu (%) for operationally defined soil fractions (exchangeable/bioavailable, Fe/Mn-
bound and organically bound Cu)
Sample ID Distance to refinery
(in m)
Exchangeable and
bioavailable fraction
Reducible fraction (bound to
Fe and Mn (hydr)oxides)
Oxidizable fraction (bound to
organic matter)
d65Cu(%)
Cu concentration
(lg g-1)
d65Cu(%)
Cu concentration
(lg g-1)
d65Cu(%)
Cu concentration
(lg g-1)
Control
(08-C2)
29,474 nd 0.4 nd 0.03 - 0.03 11.2
08-09 469 - 0.41 22.1 nd 2.70 - 0.19 244.2
08-S25 2048 nd 7.3 nd 0.56 - 0.18 328.1
09-06 1060 nd 2.4 nd 0.16 0.04 66.8
08-19 1043 - 0.43 3.5 nd 0.27 - 0.2 118.7
08-12 1030 0 2.1 nd 0.13 - 0.08 62.2
08-S15 846 0.22 12.1 0 1.29 0.22 188.9
08-20 1968 - 0.22 9.5 0.07 0.91 - 0.08 151.8
08/03 103 - 0.12 33.7 - 0.43 4.01 0.1 328.1
08-07 450 nd 6.8 nd 0.54 - 0.03 299.0
nd = not determined
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Environ Geochem Health
Ni emission from the refinery by Hayman (2009).
Hayman (2009) has shown that the metal deposition
center is about 1100 m ENE of the refinery chimney.
Furthermore, the wind-driven transport has also been
proposed by previous studies tracing the source of
airborne metals emitted by the Clydach Ni refinery
from the metal accumulation in plant biomass (Good-
man and Roberts 1971; Goodman et al. 1975).
The highest soil Pb and Co concentrations south-
west of the refinery indicate a different source than
that for Ni, Cu, Cr and Zn. As a clear evidence is
missing causing such high Pb and Co concentrations in
the village of Clydach, we can only speculate about
potential sources. The high Co soil concentration in
vicinity to an urban area might be linked to traffic
emissions and burning of fossil fuels (Smith and
Carson 1981). The source for high Pb could be related
to historic Pb processing refineries lower down the
valley (Davies 1997).
Metal soil status
Although soil concentration of most metals in vicinity
of the Ni refinery can be classified as moderately to
extremely contaminated, the majority of the metals are
not bioavailable. Mobility and bioavailability of
metals decrease in order of the extraction sequence
(Tessier 1979) where the exchangeable fraction indi-
cates the metals most available for plant uptake, while
metals bound to Fe/Mn (hydr)oxides and incorporated
into organic matter are less mobile and thus less
bioavailable.
The bioavailable metal fraction can be grouped into
metals with low and high bioavailability relative to
their total soil content. Zinc, Co and Ni are
with 23–37% of the total metal soil content highly
bioavailable, whereas only 1.3–4.5% of Cu, Cr and Pb
are bioavailable. Low or high, each bioavailable metal
fraction correlates with its total soil content. This
correlation is attributed not only to specific soil
processes (e.g., adsorption, reduction, complexation)
but also to the critical soil metal load from anthro-
pogenic or natural sources (Rieuwerts et al. 1998,
Kaasalainen and Yli-Halla 2003, Kabata-Pendias
2010, Kim et al. 2015). It has been observed that
metals from anthropogenic sources tend to be more
bioavailable than those of pedogenic origin (Kaasa-
lainen and Yli-Halla 2003, Kabata-Pendias 2010).
Thus, the high bioavailability of Ni and Zn can clearly
be explained by the anthropogenic input of these
metals to the soil from the long-term operation of the
Ni refinery at Clydach. Although the bioavailability of
Cu and Cr is extremely low, the spatial distribution
patterns (Fig. 2) show that both metals probably orig-
inated also from refining operations in this region. The
low bioavailability of Cr, Pb and Cu is mainly related
to their high affinity to adsorb on soil oxide minerals
and the formation of strong metal–organic complexes
(Rieuwerts et al. 1998; Kim et al. 2015).
Organically bound metals are the largest soil
fraction ([ 50%). Biogeochemical soil processes
(e.g., redox reactions, assimilation, adsorption and
complexation) transform inorganic metal forms
R² = 0.7625
-0.6
-0.4
-0.2
0.0
0.2
0.4
-0.6 -0.4 -0.2 0 0.2 0.4
δ65C
u org
anic
(‰)
δ65Cubioavailable (‰)
(A)
(B)
Fig. 7 aWhisker-box plot of d65Cu of the bioavailable (beige)and the organically bound (yellow) soils fraction. For the
whiskers plot, the central line marks the median value. The
lower quartile (25th percentile) and upper quartile (75th
percentile) of the dataset and the whiskers present the most
extreme data points. b Relationship between d65Cu of the
bioavailable soil fraction and the d65Cu of the organically
bound soils fraction. The dashed line describes the linear fit. The
error bars represent the root-mean-square of 0.12% for replicate
sample analysis
123
Environ Geochem Health
originated from anthropogenic sources to metal–
organic complexes (Pandey et al. 2000; Nierop et al.
2002; Kabata-Pendias 2010). The sequence of Cr[Pb[Cu[Ni[Zn[Co for the organically bound
soil fraction describes the stability of metal–organic
complexes based on the metal ion radii, redox stability
and pH. Metals with large ion radius such as Pb are
more retained by organic matter than those with small
ion radius such as Co (Rieuwerts et al. 1998).
Although Cr has a small ion radius, its high stability
with soil organic matter results from redox changes of
Cr(VI) to Cr(III) by organic matter (Wittbrodt and
Palmer 1996; Gustafsson et al. 2014). A substantial
part of soil organic matter is comprised of fulvic and
humic acids which have high metal binding capacity.
More specifically, Zn, Pb, Ni and Cu (\ 95%) bind
predominantly on fulvic acid (Donisa et al. 2003;
Boruvka and Drabek 2004; Lalas et al. 2018). The
binding preference of Cr on humic acid or fulvic acid
depends on the Cr concentrations (Donisa et al. 2003).
Therefore, we can surmise that a larger fraction of
organically bound metals are associated with fulvic
rather than humic acid. Nevertheless, the stability of
metal–organic complexes depends on the soil pH and
is generally low for Ni, Zn, and Co and high for Cu, Pb
and Cr at pH between 5 and 7.1 (Kim et al. 2015).
Biogeochemical soil cycling based on Cu isotope
signature
The d65Cu values of three extracted soil fractions
(bioavailable, Fe/Mn (hydr)oxides bound and organ-
ically bound Cu) provide a more comprehensive
framework for soil biogeochemical processes. Copper
concentrations in the extracted soil fractions alone are
not sufficient to determine the processes of Cu cycling
in the soils. During the refining process especially
during the early years of operation, aeolian particles
were emitted and deposited in the soils (Perkins 2011).
Slow dissolution of the deposited aeolian particles
releases Cu into the bioavailable soil fraction
(Fig. 4a). As metallurgic processes do not isotopically
fractionate Cu (Gale et al. 1999; Mattielli et al. 2006),
any soil Cu originated from refining processes should
be isotopically similar to the ore material. Sudbury
sulfide ores used as feeding material in the Ni refinery
have an average d65Cu of- 0.32 ± 0.54% (Christof-
fersen 2017) which matches d65Cu of the bioavailable
soil fraction (d65Cumean = - 0.16% ± 0.25%,
n = 6).
Once released in a soluble form, most of the
bioavailable Cu is scavenged by soil organic matter.
This is supported by our observation that more than
95% of Cu is present in the organically bound soil
fraction. Aqueous Cu binds to the hydroxyl functional
groups of dissolved organic matter and forms
stable chelate complexes, which decrease the bioavail-
ability of Cu (Davis 1984; Pandey et al. 2000; Nierop
et al. 2002). Even without redox changes, the coordi-
nation of ligand complexes causes Cu isotope frac-
tionation. The stronger bonding environments
between Cu and dissolved organic matter favor the
heavy 65Cu (Vance et al. 2008; Bigalke et al. 2010a, b;
Ryan et al. 2014). The strong positive correlation
between d65Cu of the bioavailable and d65Cu of the
organically bound Cu fraction (Fig. 7b) confirms that
complexation of Cu in organic matter leads to the
enrichment of heavy 65Cu. For the studied soils, the
magnitude of Cu isotopic fractionation, expressed as
D65Cuorganic-bioavailable, of ?0.12 ± 0.13% corre-
sponds to experimentally determined Cu isotope
fractionation for Cu complexation by different types
of organic ligands (D65Cu ?0.14 to ?0.84%; Ryan
et al. 2014).
Conclusion
Our integral approach of sequential extractionmethod,
spatial metal distribution maps and Cu isotope anal-
ysis clearly elucidates the source, bioavailability, and
flux of metals in polluted top soils. The results show
that after more than 100 years of metallurgical
industry in the Lower Swansea Valley, the intensity
of soil pollution is high for Cu, Ni and Pb and often
exceeds guideline values for total metal soil concen-
trations. The spatial distribution of the metals in these
top soils indicates two sources with the primary point
source being the long-term metallurgical industry in
this area. Significant proportion of soil metals ([ 50%
of total metal soil concentration) is associated with soil
organic matter which is less bioavailable and thus
potentially minimizes the ecological risk and health
risk for human.
The difference in d65Cu between bioavailable and
organically bound Cu is attributed to the complexation
of Cu with organic matter after the dissolution of the
123
Environ Geochem Health
deposited aeolian particles originating from refining
practices. Thus, isotope systematics of Cu help explain
biogeochemical process of Cu related to a long-term
pollution in topsoils.
In future studies, systematic monitoring and more
intensive sampling are required to evaluate any
prospective alteration of the metal distribution,
bioavailability and metal flux in these soils. This
would also include sampling of soil profiles to assess
the vertical impact of the metal pollution.
Acknowledgements The project was supported by the
Department of Earth Sciences, Oxford University. We thank
Phil Holdship for assisting with the ICP-MS analysis. We also
thank two anonymous reviewers for their constructive feedback.
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